In computational neuroscience it is currently common practice, when using both the GLMs and LVMs, to assume that the spike count data at each time stamp are Poisson distributed. While the Poisson assumption works well in the majority of cases [1]}, the Poisson model is established on the assumption that at each time stamp the mean spike count is equal to its variance. This ignores the fact that in some cases the variance of the spike count could be much larger than its mean [2]}, that is, the data is over-dispersed. As an alternative, the Negative Binomial (NB) model has been proposed as a solution to handling over-dispersion cases when modeling spike count data [3]}.
